Characterization and Prognosis of Biological Microenvironment in Lung Adenocarcinoma through a Disulfidptosis-Related lncRNAs Signature

Background The role of disulfidptosis-related lncRNAs remains unclear in lung adenocarcinoma. Methods Analysis in R software was conducted using different R packages, which are based on the public data from The Cancer Genome Atlas (TCGA) database. The transwell assay was used to evaluate the invasion and migration abilities of lung cancer cells. Results In our study, we identified 1401 lncRNAs significantly correlated with disulfidptosis-related genes (|Cor| > 0.3 and P < 0.05). Then, we constructed a prognosis model consisting of 11 disulfidptosis-related lncRNAs, including AL133445.2, AL442125.1, AC091132.2, AC090948.1, AC020765.2, CASC8, AL606834.1, LINC00707, OGFRP1, U91328.1, and GASAL1. This prognosis model has satisfactory prediction performance. Also, the risk score and clinical information were combined to develop a nomogram. Analyses of biological enrichment and immune-related data were used to identify underlying differences between patients at high-risk and low-risk groups. Moreover, we noticed that the immunotherapy nonresponders have higher risk scores. Meanwhile, patients at a high risk responded more strongly to docetaxel, paclitaxel, and vinblastine. Furthermore, further analysis of the model lncRNA OGFRP1 was conducted, including clinical, immune infiltration, biological enrichment analysis, and a transwell assay. We discovered that by inhibiting OGFRP1, the invasion and migration abilities of lung cancer cells could be remarkably hindered. Conclusion The results of our study can provide directions for future research in the relevant areas. Moreover, the prognosis signature we identified has the potential for clinical application.


Introduction
Worldwide, lung cancer is one of the most common cancers, and its incidence is still increasing [1]. Known as a multifactorial disease, lung cancer involves both environmental and genetic factors [2]. Among all subtypes, lung adenocarcinoma (LUAD) is the most predominant type. Despite signifcant medical advances, the prognosis of some patients with LUAD remains unsatisfactory [3]. Moreover, the pathogenesis of LUAD is largely unknown, and early diagnosis is still insufcient, which to some extent leads to treatment challenges for LUAD [4]. Consequently, identifying the genes linked to LUAD may improve the prognosis, diagnosis, and treatment of the disease.
Noncoding RNA with a length of over 200 bases is known as long noncoding RNA (lncRNA), which is famous for its widespread regulatory efects [5]. LncRNAs have multiple efect patterns including competitive endogenous RNA (ceRNA) mechanisms, protein-binding, transcriptional regulation, and so on [6]. In addition, many studies have indicated that lncRNAs may contribute to cancer development. For instance, Kong et al. discovered that lncRNA CDC6 could promote breast cancer progression through the ceRNA mechanism (miR-215/CDC6) [7]. Yuan et al. found that the lncRNA TLNC1 could accelerate liver cancer progression by hampering the p53 signaling pathway [8]. In lung cancer, Pan et al. found that the lncRNA JPX can promote lung cancer development through the miR-33a-5p/ Twist1 axis [9]. Gao et al. noticed that lncRNA PCAT1 could inhibit radioimmune responses by regulating cGAS/STING signaling [10]. Hua et al. discovered that the lncRNA LINC01123 promotes proliferation and aerobic glycolysis by ceRNA mechanisms (miR-199a-5p/c-Myc axis) [11]. Recently, Liu et al. noticed a novel cell death form named "disulfdptosis" cell death, which is due to the aberrant accumulation of intracellular disulfdes dependent on SLC7A11 [12]. Disulfdptosis is diferent from apoptosis and ferroptosis, which were previously uncharacterized. Terefore, prospective exploration of the lncRNA that regulates disulfdptosis can provide direction for future research in this feld and reveal possible targets.
In our study, we identifed 1401 lncRNAs signifcantly correlated with disulfdptosis-related genes (|Cor| > 0.3 and P < 0.05). Ten, we constructed a prognosis model consisting of 11 disulfdptosis-related lncRNAs, including AL133445.2, AL442125.1, AC091132.2, AC090948.1, AC020765.2, CASC8, AL606834.1, LINC00707, OGFRP1, U91328.1, and GASAL1. Also, the risk score and clinical information were combined to develop a nomogram. Analyses of biological enrichment and immune-related data were used to identify underlying diferences between patients in high-risk and low-risk groups. Moreover, we noticed that the immunotherapy nonresponders have higher risk scores. Meanwhile, patients at high risk responded more strongly to docetaxel, paclitaxel, and vinblastine. Furthermore, further analysis of the model lncRNA OGFRP1 was conducted, including clinical, immune infltration, biological enrichment analysis, and a transwell assay. We discovered that by inhibiting OGFRP1, the invasion and migration abilities of lung cancer cells could be remarkably hindered.

Data Collection.
Te public data of LUAD patients were downloaded from the Cancer Genome Atlas database (TCGA)-KIRC project. Te original transcriptome data form is "STAR-Counts." Te original clinical data form is "bcr-xml." For the transcriptome data, the R code of the authors was used for data normalization. Clinical data were arranged using the Perl code. Te distinction between coding genes and lncRNA is based on a reference genome fle (GRCh38.gtf ). Tumor stemness data were obtained from the previous study [13].

Collection of the Disulfdptosis-Related Genes and lncRNAs.
Te list of disulfdptosis-related genes was collected from the previous study conducted by Liu and their colleagues [12]. Correlation analysis was used to identify the disulfdptosis-related lncRNAs. For specifc disulfdptosisrelated genes, the lncRNAs with |cor| > 0.3 and P < 0.05 were regarded as disulfdptosis-related lncRNAs. Cytoscape software was used to visualize the coexpression network of disulfdptosis-related genes-lncRNAs [14].

Construction of the Prognosis Model.
As a frst step, the patients were randomly assigned to training and validation cohorts. Genes associated with prognosis were identifed using univariate Cox regression analysis (P < 0.05). Te fnal variables were optimized through the use of LASSO regression. Finally, multivariate Cox regression analyses were used to construct a prognosis model with the formula of "risk score � lncRNA A * Coef A + lncRNA B * Coef B + . . . + lncRNA N * Coef N." 2.4. Nomogram Plot. Te nomogram was created by combining the risk score and clinical information to enhance its clinical applicability. A calibration plot was used to evaluate whether the nomogram predicted survival accurately.

Biological Enrichment Analysis.
Gene set enrichment analysis (GSEA) was utilized to perform biological enrichment analysis based on multiple gene sets [15].

Cell Culture and Quantitative Real-Time PCR (qPCR).
Te lung cancer cell lines A549 and PC-9 used in this study were stored in our laboratory and cultured under conventional conditions (5% CO 2 and 37°C). To produce cDNA, total RNA was extracted and reverse transcribed using a Universal RNA Extraction Kit (TaKaRa, Shanghai, China). Te primers used for qPCR are shown in Supplementary fle 1.

Results
Te fowchart of our study is shown in Figure 1.

Prognosis
Model. Our frst step was to divide the LUAD patients into 1 :1 training and validation cohorts based on the TCGA data. First, we identifed prognosis-related lncRNAs using univariate Cox regression analysis in the training cohort. Ten, the LASSO regression analysis was applied to reduce data dimensions ( Tere were also more deaths in the high-risk group (Figure 3(i)). Compared to low-risk patients, high-risk patients had a worse survival rate (Figure 3 Figures S1-S3. In univariate and multivariate analyses, the risk score was an independent predictor of patient survival ( Figure S4).

Biological Enrichment.
Next, biological diferences between high-and low-risk groups were investigated. GSEA showed that the pathways of hypoxia, mitotic spindle, glycolysis, epithelial-mesenchymal transition (EMT), G2M checkpoint, MYC target, mTORC1 signaling and MYC target a were activated in high-risk patients ( Figure 5(a)). For GO reference terms, the terms of sister chromatid segregation, mitotic nuclear division, chromosome centromeric region, nuclear chromosome segregation, chromosome segregation, and mitotic sister chromatid segregation were upregulated in the high-risk patients (Figures 5(b)-5(g)).

Genomic Instability and Drug Sensitivity Analysis.
Genomic instability is another important factor afecting tumor progression. Terefore, we explored the genomic features in high-and low-risk patients. Results showed that    risk score was positively correlated with TMB, mRNAsi, and EREG-mRNAsi, indicating that the patients with high-risk scores might have a worse genomic instability (Figures 7(a)-7(d)). In a drug sensitivity analysis, vinblastine, docetaxel, and paclitaxel seemed to be more sensitive to patients with high-risk cancers (Figure 7(e)).

Further
Exploration of OGFRP1. Ten, we selected OGFRP1 for further analysis. We found the OGFRP1 was upregulated in LUAD tumor tissue (Figure 8     Genetics Research of ssGSEA showed that OGFRP1 was positively correlated with T2 cells but negatively correlated with B cells, TFH, CD8+ T cells, cytotoxic cells, T cells, and T1 cells (Figure 8(e)). Biological enrichment analysis showed that OGFRP1 was positively correlated with MYC targets, the mitotic spindle, E2F targets, G2M checkpoint, and glycolysis (Figure 8(f )). Clinical analysis showed a negative correlation between OGFRP1 and N stage. Te knockdown efciency of OGFRP1 is shown in Figure S6, and the sh#2 was selected for further experiments. Ten, we performed a transwell assay. A signifcant reduction in lung cancer invasion and migration was observed when OGFRP1 was inhibited (Figure 8(g)).

Discussion
Globally, lung cancer remains a major public health concern. Lung cancer is a multifactorial disease whose pathogenesis remains unclear. With the development of molecular biology, people have gradually explored the mechanisms of cancer occurrence and development and developed promising targeted therapies for specifc targets. Consequently, exploring possible targets at the molecular level is of great signifcance.
To the best of our knowledge, this is the frst study to examine the role of disulfdptosis-related lncRNAs in LUAD. In our study, we identifed 1401 lncRNAs signifcantly correlated with disulfdptosis-related genes (|Cor| > 0.3 and P < 0.05). Ten, we constructed a prognosis model consisting of 11 disulfdptosis-related lncRNAs, including AL133445.2, AL442125.1, AC091132.2, AC090948.1, AC020765.2, CASC8, AL606834.1, LINC00707, OGFRP1, U91328.1, and GASAL1. Also, the risk score and clinical information were combined to develop a nomogram. Analyses of biological enrichment and immune-related data were used to identify underlying diferences between patients at high-risk and low-risk. Moreover, we noticed that the immunotherapy nonresponders have higher risk scores. Meanwhile, patients at high risk responded more strongly to docetaxel, paclitaxel, and vinblastine. Furthermore, further                analysis of the model lncRNA OGFRP1 was conducted, including clinical, immune infltration, biological enrichment analysis, and transwell assay. We discovered that by inhibiting OGFRP1, the invasion and migration abilities of lung cancer cells could be remarkably hindered.
Our results identifed the role of 11 model lncRNAs in LUAD, which are associated with the disulfdptosis process. LncRNAs have been implicated in cancer in some cases. For example, the lncRNA AC090948.1 was found to be related to lipid metabolism, cuproptosis, and immunity in cancers [25][26][27]. Hu et al. noticed that AC020765.2 is related to autophagy in lung cancer [28]. Jiang et al. discovered that the inhibition of CASC8 could afect lung cancer progression and osimertinib sensitivity in a FOXM1-dependent manner [29]. Moreover, Zheng et al. found that AL606834.1 was associated with ferroptosis in lung cancer [30]. Ma et al. demonstrated that LINC00707 can promote lung cancer development by regulating Cdc42 [31]. Our results indicated that these model lncRNA are associated with the disulfdptosis process, which might provide a novel understanding of their role in lung cancer.
GSEA showed that the pathways of hypoxia, mitotic spindle, glycolysis, EMT, G2M checkpoint, E2F target, MYC target, mTORC1 signaling, and MYC target were activated in high-risk patients. Local hypoxia is an important characteristic of tumors. In lung cancer, Shi et al. found that YTHDF1 is associated with hypoxia adaptation, as well as lung cancer progression [32]. Zhang et al. noticed that in the absence of oxygen, bone marrow-derived mesenchymal stem cells can induce lung cancer metastasis through exosomal miRNAs and EMT pathways [33]. Yang et al. discovered that the FOXP3 could activate the Wnt/β-catenin signaling and EMT to promote lung cancer malignant phenotypes [34]. Liu et al. noticed that EMT can be activated by IL-6 depending on the NF-κB/TIM-4 axis, therefore, facilitating lung cancer metastasis [35]. Liu et al. found that the interaction between TRIB2 and PKM2 can promote lung cancer progression by regulating the aerobic glycolysis process [36]. Hua et al. demonstrated that lncRNA-AC020978 induced by hypoxia can enhance lung cancer development through glycolytic metabolism regulated by the PKM2/HIF-1α axis [37]. Tantai et al. discovered that PHLPP2 ubiquitylation can be modifed by TRIM46, therefore, enhancing lung cancer glycolysis and chemoresistance [38].
Te infuence of risk score on immune infltrating cells may be one of the reasons for the prognosis diferences in diferent risk groups. Zhang et al. noticed that the macrophage polarization regulated by SPP1 can lead to immune escape in LUAD [39]. Chen et al. discovered that exosomal-circUSP7 derived from lung cancer cells can result in CD8+ T cell dysfunction, therefore, afecting the efciency of anti-PD-L1 therapy [40]. Fang et al. found that IDO1 could downregulate NKG2D to hamper NK cells function, further inhibiting lung cancer development [41].
Although our analysis is based on high-quality data and rigorous analysis, some limitations cannot be ignored. First, the list of disulfdptosis-related genes was collected from the previous study conducted by Liu and their colleagues. However, with the deepening of relevant research, there will be more and more potential genes that regulate defective protein synthesis. Second, immune infltration analysis is performed using a variety of bioinformatics algorithms. However, bioinformatics algorithms cannot fully quantify the actual situation inside tumors.

Data Availability
Te datasets generated and/or analyzed during the current study are available in Te Cancer Genome Atlas database repository (https://portal.gdc.cancer.gov/) and are available from the corresponding author on reasonable request.